- End-to-end ownership of at least one production ML deliverable (recommender, NLP, personalization, segmentation, or GenAI feature) including how it was evaluated and what changed after launch.

- Familiarity with ML frameworks and data engineering tools, including ML and NLP systems.

- Hands-on delivery of scalable data pipelines, including concrete scale and complexity details (volumes, SLAs, latency/freshness targets, backfill strategy, failure handling).

Key Responsibilities :

- Own the endtoend delivery of at least one production ML product (e.g., recommender, NLP, personalization, segmentation, or GenAI feature), including design, implementation, evaluation, launch, and postlaunch impact analysis.

- Develop and maintain scalable data pipelines, specifying volume, SLA, latency/freshness targets, backfill strategies, and robust failure handling.

- Apply appropriate ML frameworks and dataengineering tools to build, test, and deploy models in a production environment.

Required Skills :

- Proficiency with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikitlearn) and NLP libraries.

- Strong experience building and operating data pipelines, with an understanding of scale, performance, and reliability considerations.

- Ability to define and track evaluation metrics, and to interpret model performance changes after deployment.